edip-documentation documentation · edip is an easy to use image effects processing programme with...

Edip-documentation DocumentationRelease latest

November 29, 2015

Contents

1 E.D.I.P presentation 31.1 E.D.I.P the Easy Digital Imaging Processing program . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 To know about imaging processing 52.1 The pixels mystery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.2 The Convolution kernels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.3 Thresholding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.4 Binary images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3 Files 133.1 Supported image file formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.2 Load images files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.3 Saving image files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4 Images Files Merging 154.1 Files merging Blend algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154.2 Files merging Screen algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154.3 Files merging Darker algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164.4 Files merging Lighter algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164.5 Files merging Add algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164.6 Files merging Add modulo algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164.7 Files merging Subtract algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174.8 Files merging Subtract modulo algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

5 Simple Settings 195.1 Intensity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195.2 Colorspace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

6 Simple Filters 216.1 Edip built-in filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216.2 Simple Grayscaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266.3 Simple Colorscaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

7 Advanced Configurable Filters 317.1 Kernel filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317.2 Photography filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337.3 Morphological filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357.4 Canny filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367.5 Color contours filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

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8 Advanced Configurable Color Filters 378.1 Mulitply Colors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378.2 Multiply global intesity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378.3 Set image in color tone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

9 Drawing functionalities 399.1 Polylines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399.2 Rectangle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399.3 Circle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399.4 Ellipse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399.5 Polygon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409.6 Star . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409.7 Text . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

10 Credits 4110.1 Family . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4110.2 OpenCV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4110.3 Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

11 Indices and tables 43

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Edip-documentation Documentation, Release latest

Contents:

Contents 1


2 Contents

CHAPTER 1

E.D.I.P presentation

Program Edip

Version 1.0.0

Author Eddie Brüggemann <[email protected]>

Language C++

Release 24/11/2015

License GPLv3

1.1 E.D.I.P the Easy Digital Imaging Processing program

Edip is an easy to use image effects processing programme with severals functionnalities.

Edip permit to:

• Rotate your image over 90° to the left or to the right.

• Mirror your image, so that:

– The left side from your image is copy, mirror likewise, on the right side.

– The right side from your image is copy, mirror likewise, on the left side.

– The upper part of your image is copy, mirror likewise, on the bottom part.

– The bottom part of your image is copy, mirror likewise, on the upper part.

• Zoom on a selected center or the default image center.

• Flipping your image.

– From left to right.

– From right to left.

– From up to down.

– From down to up.

Edip permit to change the image color intensity and some phenomenal color spaces settings.

• Color intensity

– You can set the red intensity.

– You can set the green intensity.

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mailto:[email protected]


– You can set the blue intensity.

– You can set the global intensity.

• Phenomenal color spaces settings

– You can set the Lightness from your image.

– You can set the Hue from your image.

– You can set the Saturation from your image.

– You can set the Brightness from your image.

Edip permit to apply severals built-in filters to your image by simply selecting the wanted filter in thelist and pressing the apply button.

The built-in filters are divide into 3 differents categories:

• normal filters

You can apply over 30 differents filters to your image.

• Grayscaling filters

You can grayscale your image according 6 differents algorithms.

• Colorscaling filters

You can colorscale your image in 6 differents colors (red, green, blue, yellow, pink, turquoise)according differents algorithms for every color.

In addition Edip provide many fully configurable filters which you can apply to your image:

• Kernel filters.

• Photography filters.

• Morphological filters.

• Canny filter.

• Color contours filter.

• Multiply colors.

• Multiply global intensity.

• Set image in color tone.

Edip provide some basic draw functionalities to draw onto your image in the wanted color with thewanted thickness or to fill your form.

• Polylines.

• Rectangle.

• Circle.

• Ellipse.

• Polygone: normal or strikethrough.

• Star: normal, strikethrough, flower likewise, stroked.

• Text.

4 Chapter 1. E.D.I.P presentation

CHAPTER 2

To know about imaging processing

2.1 The pixels mystery

An (raw) image is composed from pixels as you know.

Every pixel can be encoded on:

2.1.1 Basic pixel encoding

On a single value between 0 and 255: This is the case of gray images.

In this case every pixel can take a value from 0 (black) to 255 (white).

To create every grayscale range value.

On 3 or 4 channels of colors for every pixel.

These channels are:

• The red channel which can take a value between 0 (black) and 255 (fully red).

• The green channel which can take a value between 0 (black) and 255 (fully green).

• The blue channel which can take a value between 0 (black) and 255 (fully blue).

• And a optional fourth channel can add an opacity value: which can take a value between0 (fully transparent) and 255 (fully opaque):

What mean that the image can contains fully transparent pixels, what permit todisplay a form which according per example your desktop background.

For every pixel: so the pixels are encoded on 3 or 4 values between 0 and 255 representing thered, green, blue and optionnaly alpha channel values.

What give us the RGB(A) color space.

This is not very intuitive and this is not the way humans think about colors.

But the red, green and blue values combined together give us a wide gamut of different colors

256 × 256 × 256 = 16777216 (differents possible colors).

Note: In fact, the human visual system is also based on the trichromatic perception of colors.

With cone cell sensitivity located around the red, gren and blue spectrum.

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This is the common pixel encoding in digital imaging.

examples:

A fully red pixel will take following values:

Channel Red Green BlueValue 255 0 0

Resulting color:

Note: Red is a base color because only one channel is set.

A fully green pixel will take following values:


Resulting color:

Note: Green is a base color because only one channel is set.

A fully blue pixel will take following values:


Resulting color:

Note: Blue is a base color because only one channel is set.

A fully yellow pixel will take following values: We mix red and green to obtainyellow color.


Resulting color:

Note: Yellow is a composite color because 2 differents channels are set.

A fully pink pixel will take following values: We mix red and blue to obtain pinkcolor.


Resulting color:

Note: Pink is a composite color because 2 differents channels are set.

6 Chapter 2. To know about imaging processing


A fully turquoise pixel will take following values: We mix the green and blue toobtain the turquoise color.


Resulting color:

Note: Turquoise is a composite color because 2 differents channels are set.

A emboss gray pixel encoded on RGB (Red, Green, Blue) look’s like this:


Resulting color:

Note: Gray is a composite color because the 3 channels are set.

Gray can be encoded on a single value: a 1 byte encoding.

2.1.2 Other pixels encoding

Otherwise they are other image coding system’s: the phenomenal color spaces.

Which are based on the concept of

• Hue.

• Saturation.

• And brightness.

These properties are more intuitive for humans.

The phenomenal color spaces have been introduced because they correspond to the way humans tend tonaturally organized colors.

With atributes like:

• Tint.

• Colorfullness.

• And brightness.

• Hue: represent the dominant color.

The name you give to a color correspond to the hue value.

– The hue value varies from 0 to 180 by representing the dominant color on a circle(0-360 degrees) which value is divide per 2 for encoding convienence.

or in others words the hue represent the tint of the color.

• Saturation: represent how vivid the color is.

The saturation varies from 0 to 255 (pure spectrum color).

The saturation represent the vivid of the colors:

2.1. The pixels mystery 7


– Low value : pastel color.

– High value : rainbow color.

max(red, green, blue) - min(red, green, blue)Saturation = ---------------------------------------------

max(red, green, blue)

• Brightness: represent the luminosity of a color.

This is an subjectiv attribute.

Note: Others phenomenal color spaces values use the concept of value or lightness as a way to chara-terize the relativ color intensity.

Note: This colors attributes try to mimic the intuitive human pereption of colors.

• Example: the HSB color space.

– The hue is the cone top circle.

– The brightness is the cone height.

– The saturation value depends from the length of the arrow which must ne oriented into thedirection of the hue value.

2.2 The Convolution kernels

A kernel is a set of weights which determine how each output pixel is calculated from a neighborhood of input pixels.

Applying a kernel filter consist of moving the kernel over each pixel of an image and multiplying each correspondingpixel by it’s associated weight.

According to that the central pixel correspond to a pixel of interest and the others to the neighborhood pixels.

The kernel is an odd matrix of values from any (odd) size limited from 3x3 to 31x31 and can be from different forms:



2.2.1 Kernels forms

• A square type kernel matrix:

1 1 1 1 11 1 1 1 11 1 1 1 11 1 1 1 11 1 1 1 1

• A diamond type kernel matrix:

0 0 1 0 00 1 1 1 01 1 1 1 10 1 1 1 00 0 1 0 0

• A cross type kernel matrix:

0 0 1 0 00 0 1 0 01 1 1 1 10 0 1 0 00 0 1 0 0

• An X type kernel matrix:

1 0 0 0 10 1 0 1 00 0 1 0 00 1 0 1 01 0 0 0 1

The art which the values are multiply depends from the operator which use the matrix, and the central pixel valuedepend from it.

The total summe of the matrix values represent the weight of the matrix and so we often adjust the value of the centralpixel to make the total weight equal to:

1 or 0

but not always because it depends from how the value are interpolate:

2.2.2 Classical kernel matrixes

• A sharpen kernel matrix:

-1 -1 -1-1 9 -1-1 -1 -1

has a weight of +1 due of the 8 x -1 values and the central +9 value.

• A Find Edges kernel matrix:

-1 -1 -1-1 8 -1-1 -1 -1

2.2. The Convolution kernels 9


has a weight of 0 due of the 8 x -1 values and the central +8 value.

• A Emboss kernel matrix:

-2 -1 0-1 1 10 1 2

has a weight of +1 due of the (-2 + -1 + 1) and (2 + 1 + 1) values and the central 1 value.

• A Mean kernel matrix:

1/9 1/9 1/91/9 1/9 1/91/9 1/9 1/9

has a weight of +1 du of the fact that all values are equal to 1.0 divide per kernel size.

• A Gaussian kernel matrix: is based on a gaussian vector: a vector of values seed from a sigma value. examplefrom a gausssian vector from size 7 with 1.2 as sigma value:

[ 0.015, 0.083, 0.236, 0.333, 0.236, 0.083, 0.015 ]

• A Kirsch kernel matrix:

Have an orientation:

– East orientation:

-3 -3 5-3 0 5-3 -3 5

– West orientation:

5 -3 -35 0 -35 -3 -3

– North orientation:

-3 -3 -3-3 0 -35 5 5

– South orientation:

5 5 5-3 0 -3-3 -3 -3

• A Sobel kernel matrix:

– horizontally:

-1 -2 -10 0 01 2 1

– Vertically:

-1 0 1-2 0 2-1 0 1



• A Laplacian kernel matrix:

0.5 1.0 0.51.0 6 1.00.5 1.0 0.5

2.3 Thresholding

The use of thresholding can be used to obtain a binary image (black and white image) by setting a value:the threshold value.

In relationship to this value, all pixels values (encoded on a single value) greater as this value are set towhite.

All pixels littler are set to black.

What produce a binary image map of the source image.

Note: Thresholding can be invert so that greater values are set to black and littler are set to white.

What permit to get a White and a Black version of the filters producing a binary image.

It exist algorithms which can determine the best threshold value based on the analysis of a grayscaleimage:

• OTSU algorithm. (As in the Binary White OTSU or Binary Black OTSU filters.)

• TRIANGLE algorithm. (As in the Binary White TRIANGLE or Binary Black TRIANGLE filters.)

Note: You can take the average value from this 2 algorithms computed threshold values

As in the Binary White Average or

the Binary Black Average filters.

2.4 Binary images

Binary images are strictly black and white images with as convention that:

The foreground from the image is represented in white.

The background from the image is represented in black.

2.3. Thresholding 11

CHAPTER 3

Files

3.1 Supported image file formats

Edip support normally following images files formats to load into the program for processing:

• bmp, dib (bitmap image file.)

• jpg, jpeg, jpe (Join photographic Expert Group.)

• png (Protable Network Graphics.)

• tiff, tif (Tag(ged) Image File (Format).)

• ras, sr (Sun Raster).

• pbm, pgm, ppm (Portable Image Format.)

• webp.

The image file is loaded into the program and converted into RGB, (internally BGR) format or RGBA(internally BGRA) if an alpha channel is available in the image.

Note: For supporting an image filetype the required library need to be installed except for:

• bmp, dib (bitmap image file.)

• pbm, pgm, ppm (Portable Image Format.)

• ras, sr (Sun Raster).

Which are always supported.

3.2 Load images files

You can load an image file to process it into Edip by clicking on the Open button in the buttonbar,

through the menu item Files → Open file or by hitting the shortcut Ctrl + O.

You can start the program with am image file path as argument:

$ edip image_filepath

You can open an recent image file by using the menu item Files → Recent files.

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3.3 Saving image files

You can save an image file by pressing the button Save in the button bar,

trough the menu item Files → Save file or by hitting the shortcut Ctrl + S.

Then you can set the output size of the image which default to the originally image size.

And setting some options in relatinship to the format if available.

3.3.1 Image file format saving options

png image file saving options

• Compression: from level from 0 to 9. A higher value means a smaller size and longer compression time.Default value is 3.

• Strategy:

– Default.

– Filtered.

– Huffman only.

– RLE.

– Fixed.

• Bilevel:

You can only store an image as bilevel if the image is a binary (black and white) image.

Otherwise the result will be undefined.

jpeg image file saving options

• Quality: it can be a quality from 0 to 100 (the higher is the better). Default value is 95.

• Features:

– Progressiv (disabled per default).

– Optimize (disabled per default).

• RST Restart: JPEG restart interval, 0 - 65535, default is 0 - no restart.

• LUMA quality: Separate luma quality level, 0 - 100, default is 0 - don’t use.

• CHROMA quality: Separate chroma quality level, 0 - 100, default is 0 - don’t use.

xpm image file saving options

• Binary: For PPM, PGM, or PBM, it can be a binary format flag, 0 or 1. Default value is 1.

webp image file saving options

• Quality: For WEBP, it can be a quality from 1 to 100 (the higher is the better). By default and for quality above100 the lossless compression is used.

14 Chapter 3. Files

CHAPTER 4

Images Files Merging

Edip provide severals files merging algorithms which permit to combine 2 input files to an single outputfile through an image mixing algorithm.

You can choose 2 differents input image files, in any supported format, with giving eventual settings, tomerge it into a single ouput image file.

The differents images must have the same size to be interpolated, so Edip resize the output image to thesame size, to the littler image file size from the twos per default.

But you can set the output size you want for your output image.

Then you can save the result of the image files merging to any supported format, by clicking on the Savefile button.

4.1 Files merging Blend algorithm

Interpolate 2 images file with the constant alpha to generate an output image:

Result = Image1 * alpha - (1-alpha) * Image2

• You must set the value of alpha which default to 0.5 between 0.01 and 1.0.

The images are converted to the same size (to the littler image) was is required for blend operation.

Note: You can use the button Preview result to preview your output image.

If the output size is greater than 800x600 pixels the preview image is resize to be included in the imagepreview window.

4.2 Files merging Screen algorithm

Superimpose two inverted images on the top of each other:

Result = MAX - ((MAX-Image1) * (MAX-Images2) / MAX)

The images are converted to the same size (to the littler image) was is required for screen operation.


15



4.3 Files merging Darker algorithm

Interpolate 2 images file and produce an output image with the darkest pixels values:

Result = min( Image1, Image2 )

The images are converted to the same size (to the littler image) was is required for darker operation.



4.4 Files merging Lighter algorithm

Interpolate 2 images file and produce an output image with the lightest pixels values:

Result = max( Image1, Image2 )

The images are converted to the same size (to the littler image) was is required for lighter operation.



4.5 Files merging Add algorithm

Add 2 images, dividing the result by scale and adding offset to generate an output image:

Result = (Image1 + Image2) / scale + offset

• You must set the value of scale which default to 1.0 between 0.01 and 8.0.

• You must set the value of offset which default to 0 between 0 and 255.

The images are converted to the same size (to the littler image) was is required for add operation.



4.6 Files merging Add modulo algorithm

Add 2 images and make the euclidian division of the result per the maximal:

16 Chapter 4. Images Files Merging


Result = ( Image1 + Image2 ) % MAX

The images are converted to the same size (to the littler image) was is required for add modulo operation.



4.7 Files merging Subtract algorithm

Subtract 2 images, dividing the result by scale and adding offset to generate an output image:

Result = (Image1 - Image2) / scale + offset

• You must set the value of scale which default to 1.0 between 0.01 and 8.0.

• You must set the value of offset which default to 0 between 0 and 255.

The images are converted to the same size (to the littler image) was is required for sub operation.



4.8 Files merging Subtract modulo algorithm

Subtract 2 images and make the euclidian division of the result per the maximal:

Result = ( Image1 - Image2 ) % MAX

The images are converted to the same size (to the littler image) was is required for sub modulo operation.



4.7. Files merging Subtract algorithm 17


18 Chapter 4. Images Files Merging

CHAPTER 5

Simple Settings

You will find the simple settings on the right sidebar from Edip.

You can switch between Intensity and Colorspace with the corresponding buttons.

5.1 Intensity

Here you can change the color intensities with the corresponding scales.

• Red: from -255 to 255.

Set the wanted red intensity change and press the Apply button.

– If the selected value is greater than 0 it will add the selected value to the red channel from allpixels of your image.

– If the selected value is littler than 0 it will subtract the selected value from the red channelfrom all pixels of your image.

• Green: from -255 to 255.

Set the wanted green intensity change and press the Apply button.

– If the selected value is greater than 0 it will add the selected value to the green channel fromall pixels of your image.

– If the selected value is littler than 0 it will subtract the selected value from the green channelfrom all pixels of your image.

• Blue: from -255 to 255.

Set the wanted blue intensity change and press the Apply button.

– If the selected value is greater than 0 it will add the selected value to the blue channel from allpixels of your image.

– If the selected value is littler than 0 it will subtract the selected value from the blue channelfrom all pixels of your image.

• Global: from -255 to 255.

Set the wanted intensity change and press the Apply button.

– If the selected value is greater than 0 it will add the selected value to the all channels from allpixels of your image.

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– If the selected value is littler than 0 it will subtract the selected value from the all channelsfrom all pixels of your image.

5.2 Colorspace

Here you can set:

• The Lightness from your image.

• The Hue from your image.

• The Saturation from your image.

• The Brightness from your image.

20 Chapter 5. Simple Settings

CHAPTER 6

Simple Filters

6.1 Edip built-in filters

Edip provide a lot of predefine, non-configurable built-in filters, applicable like this:

6.1.1 Pencil Sketch

The Pencil Sketch filter produce a non-photorealistic line drawing image.

6.1.2 Stylisation

The stylisation filter is an edge-aware filters which effect is not focused on photorealism but abstractregions of low contrast while preserving, or enhancing, high-contrast features.

6.1.3 Detail Enhance

The Detail Enhance filter enhances the details of a particular image.

6.1.4 Edges Preserving

The Edge Preserving filter is a smoothing filters used in many different applications.

6.1.5 Stroke Edges

The Stroke Edges filter add black strokes on some edges of the image.

6.1.6 Invert Intensity

The Invert Intensity filter invert the intensity.

This mean that the maximal value become the minimal value and so soon in the pixel values range.

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6.1.7 Light Intensity

The Light Intensity filter invert the intensity from the pixels channels average value.

So that it output an grayscale image with light effect.

6.1.8 Recolor RC (Red-Cyan)

Simulate conversion from RGB (Red, Green, Blue) to RC (red, cyan).

Blues and greens are replaced with cyans.

The effect is similar to Technicolor Process 2 (used in early color movies) and CGA Palette 3 (used inearly color PCs).

6.1.9 Recolor RGV (Red-Green-Value)

Simulate conversion from RGB (Red, Green, Blue) to RGV (red, green, value).

Blues are desaturated.

The effect is similar to Technicolor Process 1 (used in early color movies).

6.1.10 Recolor CMV (Cyan-Magenta-Value)

Simulate conversion from RGB (Red, Green, Blue) to CMV (cyan, magenta, value).

Yellows are desaturated.

The effect is similar to CGA Palette 1 (used in early color PCs).

6.1.11 Extrema Maximal

The Extrema Maximal filter compute the maximal values from 2 differents channels and set the result ona channel.

6.1.12 Extrema Minimal

The Extrema Minimal filter compute the minimal values from 2 differents channels and set the result ona channel.

6.1.13 Sharpen

The Sharpen filter sharpen the image by adding some white pixels at some edges and points of the image.

This filter is based on a Sharpen convolution matrix.

22 Chapter 6. Simple Filters


6.1.14 Sharpen More

The Sharpen More filter sharpen the image by adding some white pixels at some edges and points of theimage.

It has more impact on the image as the Sharpen filter but does the same stronger.

This filter is based on a Sharpen convolution matrix.

6.1.15 Find Edges

The Find Edges filter will result in black background image with the contours and some pixels, of thesource image, displayed in color.

This filter is based on a Find Edges convolution matrix.

6.1.16 Mean Blur

The Mean Blur filter swindle the image. The result image is blurred.

This filter is based on a Mean convolution matrix.

6.1.17 Mean Blur More

The Mean Blur More filter swindle the image. The result image is blurred.

It has more impact on the image as the Mean Blur filter but does the same stronger.

This filter is based on a Mean convolution matrix.

6.1.18 Blur

The Blur filter swindle the image.

What can remove some noise of the image or smooth an pixelize image.

6.1.19 Blur Median

The Blur Median filter swindle the image.


This filter is well know for salt and pepper noise removing.

6.1.20 Blur Gaussian

The Blur Gaussian filter swindle the image.


It use gaussian values for creating the blurring effect applying onto the image.

6.1. Edip built-in filters 23


6.1.21 Denoising

The denoising filter perform image denoising using Non-local Means Denoising with several computa-tional optimizations.

Noise expected to be a gaussian white noise.

Denoising is done by converting image to CIELAB colorspacenand then separately denoise L and ABcomponents with different parameters.

6.1.22 Erode

The Erode filter eroding the image content contours.

This filter erodes the image by minimizing the pixels neighborhood.

6.1.23 Dilate

The Dilate filter dilate the image content contours.

This filter dilates the image by maximizing the pixels neighborhood.

6.1.24 Wave Horizontally

The Wave Horizontally filter output an horizontally waved image.

6.1.25 Wave Vertically

The Wave Vertically filter output an horizontally waved image.

6.1.26 Wave Twice

The Wave Twice filter output an horizontally and vertically waved image.

6.1.27 Contours Sobel White

The Contours Sobel White filter result in an image with a white background and colored drawning wiseforeground.

All the contours strokes of the image will be displayed on a white background.

6.1.28 Contours Sobel Black

The Contours Sobel Black filter result in an image with a black background and colored drawning wiseforeground.

All the contours strokes of the image will be displayed on a black background.



6.1.29 Contours Sobel Emboss

The Contours Sobel Emboss filter result in an image with an emboss gray background and colored drawn-ing wise foreground.

All the contours strokes of the image will be displayed on a emboss gray background.

6.1.30 Emboss Sobel

The Emboss Sobel filter is an emboss filter which result looking like a kopper engraving.

Based on the Sobel operator which detect edges horizontally and or vertically.

6.1.31 Emboss Laplacian

The Emboss Laplacian filter is an emboss filter which result looking like a kopper engraving.

Based on the Laplacian operator which detect edges horizontally and vertically.

6.1.32 Binary White OTSU

The Binary White OTSU filter create an black and white image, from the source image, with whitebackground and black foreground.

Based on the OTSU image specific threshold computing algorithm.

Simply try every algorithm to find the appositely one for your image, to get the best binary image from it.

6.1.33 Binary White TRIANGLE

The Binary White TRIANGLE filter create an black and white image, from the source image, with whitebackground and black foreground.

Based on the TRIANGLE image specific threshold computing algorithm.


6.1.34 Binary White AVERAGE

The Binary White AVERAGE filter create an black and white image, from the source image, with whitebackground and black foreground.

Based on the OTSU and TRIANGLE image specific threshold computing algorithm, average.


6.1.35 Binary Black OTSU

The Binary Black OTSU filter create an black and white image, from the source image, with black back-ground and white foreground.

Based on the OTSU image specific threshold computing algorithm.


6.1. Edip built-in filters 25


6.1.36 Binary Black TRIANGLE

The Binary Black TRIANGLE filter create an black and white image, from the source image, with blackbackground and white foreground.

Based on the TRIANGLE image specific threshold computing algorithm.


6.1.37 Binary Black AVERAGE

The Binary Black AVERAGE filter create an black and white image, from the source image, with blackbackground and white foreground.

Based on the OTSU and TRIANGLE image specific threshold computing algorithm, average.

Simply try every algorithm to find the appositely one for your image,nto get the best binary image fromit.

6.1.38 Binary Contours White

The Binary Contours White filter create an black and white image, from the source image, with whitebackground and black foreground.

With as foreground all the contours strokes of the image.

Like an black and white drawning.

6.1.39 Binary Contours Black

The Binary Contours Black filter create an black and white image, from the source image, with blackbackground and white foreground.

With as foreground all the contours strokes of the image.

Like an black and white drawning.

6.2 Simple Grayscaling

Edip can grayscaling your image, to set it enterely in gray tone, according differents bases:

• Gray Scaling Average.

• Gray Scaling Maximal.

• Gray Scaling Minimal

• Gray Scaling Red.

• Gray Scaling Green.

• Gray Scaling Blue.

Edip will process all the pixels of your images to compute the wanted gray values.

To know that either a gray pixel can be encoded on a single value between 0 and 255 or

setting all channels on the same value will result in a gray pixel too.



For example let say the current pixel Edip is procesing has the following channels values:


• Gray Scaling Average:

Edip will compute the average from all channels and set the result as gray value.

So Edip compute the average value:

(32 + 128 + 192) / 3 = 117.

So the resulting pixel will be transform to have following values:


Or be encoded on a single value equal to 117.

• Gray Scaling Maximal:

Edip will take the highest value from all the channels and set the it as gray value.

So Edip compute the maximal value from the channels:

32 < 128 < 192 = 192.




• Gray Scaling Minimal:

Edip will take the lowest value from all the channels and set the it as gray value.

So Edip compute the minimal value from the all channels:

192 > 128 > 32 = 32.




• Gray Scaling Red:

Edip will take the value from the red channel and set the it as gray value.

So Edip recover the red channel value which is equal to 32:

Red = 32.




• Gray Scaling Green:

6.2. Simple Grayscaling 27


Edip will take the value from the green channel and set the it as gray value.

So Edip recover the green channel value which is equal to 32:

Green = 128.




• Gray Scaling Blue:

Edip will take the value from the green channel and set the it as gray value.

So Edip recover the blue channel value which is equal to 192:

Blue = 192.




Note: Edip will compute every pixel from your image like explain above according the choosen grayscaling base.

6.3 Simple Colorscaling

Edip can colorscaling your image, to set it enterely in a main color tone, according differents bases:

• In red tone:

– Red Scaling Average.

– Red Scaling Maximal.

– Red Scaling Minimal.

– Red Scaling Green.

– Red Scaling Blue.

• In green tone:

– Green Scaling Average.

– Green Scaling Maximal.

– Green Scaling Minimal.

– Green Scaling Red.

– Green Scaling Blue.

• In blue tone:

– Blue Scaling Average.

– Blue Scaling Maximal.

– Blue Scaling Minimal.



– Blue Scaling Green.

– Blue Scaling Blue.

• In yellow tone:

– Yellow Scaling Average.

– Yellow Scaling Maximal.

– Yellow Scaling Minimal.

– Yellow Scaling Red.

– Yellow Scaling Green.

– Yellow Scaling Blue.

• In pink tone:

– Pink Scaling Average.

– Pink Scaling Maximal.

– Pink Scaling Minimal.

– Pink Scaling Red.

– Pink Scaling Green.

– Pink Scaling Blue.

• In turquoise tone:

– Turquoise Scaling Average.

– Turquoise Scaling Maximal.

– Turquoise Scaling Minimal.

– Turquoise Scaling Red.

– Turquoise Scaling Green.

– Turquoise Scaling Blue.

Note: The pixels values computing according the differents bases are the same at by grayscaling.

6.3. Simple Colorscaling 29

CHAPTER 7

Advanced Configurable Filters

7.1 Kernel filters

7.1.1 Sharpen kernel filter

You can apply a fully configurable Sharpen kernel filter to the image.

• Configuration options:

– Kernel type:

* Square

* Diamond

* Cross

* X

– Kernel size:

The size of the kernel: per example a size of 3 correspond to a 3x3 matrix.

You can only use odd values between 3 and 31 included.

– Values factor:

The factor to multtply each kernel component with.

– Center factor:

The factor to multiply the kernel center with.

Note: You can preview the kernel you have configurated by using the Preview kernel button.

7.1.2 Emboss kernel filter

You can apply a fully configurable Emboss kernel filter to the image.


– Kernel type:

* Square

* Diamond

31


* Cross

* X

– Kernel size:




7.1.3 Mean kernel filter

You can apply a fully configurable Mean kernel filter to the image.


– Kernel type:

* Square

* Diamond

* Cross

* X

– Kernel size:




7.1.4 Gaussian kernel filter

You can apply a fully configurable Gaussian kernel filter to the image.


– Kernel type:

* Square

* Diamond

* Cross

* X

– Kernel size:



– Sigma value:

Seed value used to generate the gaussian vector on which the kernel will be based.

32 Chapter 7. Advanced Configurable Filters



7.1.5 Kirsch kernel filter

You can apply a fully configurable Kirsch kernel filter to the image.


– Direction:

* East

* West

* North

* South

– Kernel size:



– Values factor:

The factor to multtply each kernel component with.


7.2 Photography filters

7.2.1 Pencil Sketch

The Pencil Sketch filter produce a non-photorealistic line drawing image.


– Sigma S:

Value between 0.0 and 200.0.

– Sigma R:


– Shade factor:


7.2.2 Stylisation

The stylisation filter is an edge-aware filters which effect is not focused on photorealism but

abstract regions of low contrast while preserving, or enhancing, high-contrast features.


7.2. Photography filters 33


– Sigma S:


– Sigma R:


7.2.3 Detail Enhance

The Detail Enhance filter enhances the details of a particular image.


– Sigma S:


– Sigma R:


7.2.4 Edge Preserving

The Edge Preserving filter is a smoothing filters used in many different applications.


– Sigma S:


– Sigma R:


– Filter:

* Recurse filter.

* NormConv Filter.

7.2.5 Denoising

The denoising filter perform image denoising using Non-local Means Denoising with several computational optimiza-tions.

Noise expected to be a gaussian white noise.

Denoising is done by converting image to CIELAB colorspace and then separately denoise L and AB componentswith different parameters.


– Luminance factor:

Parameter regulating filter strength. Big values perfectly removes noise but also removes image details,smaller values preserves details but also preserves some noise.

– Color denoising factor:

The same as Luminance factor but for color components.



7.3 Morphological filters

Morphological operations process images according to shapes.

They apply a defined structuring element (named kernel) to the image obtaining a new image where the current pixelis computed by comparing it to his neighborhood pixels.

Note: Depending on the structuring element selected (which is fully configurable in Edip)

a morphological operation is more sensitive to one specific shape or the other.

7.3.1 Morphological operations

Dilatation

Dilatation add pixels from the background to the boundaries of the object in an image.

In dilatation, the value of the output pixel is the maximum of all pixels in the neighborhood.

Erosion

Erosion remove pixels from the foreground.

Using erosion the value of the output pixel is the minimum of all pixels in the neighborhood.

Opening

Opening is an erosion followed by a dilatation.

Opening remove small objects from an image while preserving the larger one.

Closing

Closing is a dilatation followed by an erosion.

Closing is used to remove samll holes while preserving the larger one.

Tophat

Tophat ouput is the difference between the source image and it’s opening.

Blackhat

Blackhat ouput is the difference between the source image and it’s closing.

7.3. Morphological filters 35


7.4 Canny filter

The Canny contours detection operator use 2 different threshold values:

Low threshold: The low threshold should be chosen in a way that it included all edges pixels that areconsidered as to belong to a significant image contour.

This should detect more edges that what is ideally needed in the case of the Canny algorithm.

High threshold: His role is to define the edges that belong to all important contours.

It should exclude all edges considered as outlier.

The Canny algorithm combines these two edges maps in order to produce an optimal map of contours.

It operate by keeping only edge points of the low threshold edge map for which a continuous path of edges exist,linking all edges points of the high threshold map are kept, while isolated chains of edges points in the low thresholdmap are removed.

Note: This strategy, based on the use of two threshold to obtain a binary map, is called histereris thresholding.

In addition, the Canny algorithm uses an extra strategy to improve the quality of the edge map: all edges points forwhich the gradient magnitude is not a maximum in the gradient direction are removed.

The gradient orientation is always perpendicular to the edge. Therefor, the local maximum of the gradient is thisdirection correspond to the points of maximum strength of the contours.

This explains why thin edges are obtained in tha Canny contours map.

7.5 Color contours filter

This produce the same result as the Canny filter except that you can set:

• The foreground color: the color of the detected edges.

• The background color.


CHAPTER 8

Advanced Configurable Color Filters

8.1 Mulitply Colors

You can set following factors to multiply each different color channel by it corresponding factor value.

• Multiply red channel:

Factor to multiply the red channel.

• Multiply green channel:

Factor to multiply the green channel.

• Multiply blue channel:

Factor to multiply the blue channel.

• Multiply alpha channel:

Factor to multiply the alpha channel.

8.2 Multiply global intesity

Mulitply each color channel (red, green and blue) by the given factor.

8.3 Set image in color tone

You can select a color to set the entire image in this color tone.

The color tone is applied to the image by following steps:

1. Computing the image mean value for every channel.

2. Dividing each given color by the corresponding mean value from each channel.

3. Multiply each channel by the result from step 2.

37


38 Chapter 8. Advanced Configurable Color Filters

CHAPTER 9

Drawing functionalities

Edip provide following drawing functionalities.

• You can set the color of the form to draw.

• You can set the stroke thickness of the form to draw or choose to fill your form.

9.1 Polylines

Draw a line or joined multilines, by clicking on the image with the left mouse button.

Press the right mouse button to stop drawning.

Note: If you clic on the startpoint of mutilines drawning it will join the lines to a polygon.

9.2 Rectangle

Draw an rectangle, by clicking on the image with the left mouse button, to set an anchor corner.

Then move the mouse to size it at your convenience, and click again to draw the rectangle onto yourimage.


9.3 Circle

Draw a circle, by clicking on the image with the left mouse button, to set his center.

Then move the mouse to size it at your convenience, and click again to draw the circle onto your image.


9.4 Ellipse

Draw an ellipse, by clicking on the image with the left mouse button, to set his center.

Then move the mouse to size it at your convenience, and click again to draw the ellipse onto your image.

39



9.5 Polygon

Draw a convex regular polygon: first configure what type of polygon you want to draw.

Then click on the image with the left mouse button, to set his center.

And move the mouse to size it at your convenience, and click again to draw the polygon onto your image.


Note: You can press the keys ‘$’ or ‘*’ to turn the polygon and hit the ‘=’ key to draw it.

9.6 Star

Draw a star: first configure what type of star you want to draw.

Then click on the image with the left mouse button, to set his center.

And move the mouse to size it at your convenience, and click again to draw the star onto your image.


Note: You can press the keys ‘$’ or ‘*’ to turn the star and hit the ‘=’ key to draw it.

9.7 Text

Insert some text on your image: enter your text and configure the font settings.

Then slide the text to the wanted position, and click with the left mouse button, to set the text position.

Press the right mouse button to stop editing.

40 Chapter 9. Drawing functionalities

CHAPTER 10

Credits

10.1 Family

Note: Thank’s to my beloved mother, my family and to the doctors.

Stay away from drugs: drugs destroy your brain and your life.

10.2 OpenCV

The OpenCV library provide over 300 algorithms for computer vision purpose.

The OpenCV libary was introduced in 1999 and adopted as primary development tool for computer visionby researcher and developers in computer vision.

OpenCV was originally developed at Intel by a team lead by Gary Bradsky as an initiative to advancedresearch in vision and promote the development of rich vision, CPU-intensiv applications.

1. 2006 first major release of OpenCV.

2. 2009 second major release of OpenCV.

3. 2012 reshape from OpenCV to a non-profit foundation opencv.org

Note: Thank’s to all the contributors of the OpenCV libary for providing us this exceptional tool.

10.3 Sources

• OpenCV Computer Vision Application Programming Cookbook from Robert Langarnièreat Packt Publishing.

• Learning Image Processing with OpenCV from multi-authors at Packt Publishing.

• OpenCV Computer Vision with Python from Joseph Howse at Packt Publishing.

41

http://opencv.org


42 Chapter 10. Credits

CHAPTER 11

Indices and tables

• genindex

• modindex

• search

43

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